The current main PET detector commonly employs the detecting structure in which array scintillation crystals are coupled to a photoelectric conversion device. After γ photons are incident on a crystal strip in the array of crystals, the Compton scattering or the photoelectric effect occurs, producing visible light signals. The photoelectric conversion device receives these optical signals and converts them into corresponding electrical signals. With the electrical signals, the coordinates (X, Y) of the incident position of the photon γ can be calculated. The number of the crystal strip where the γ photon event occurs is obtained based on the coordinates, to determine the Line of Response (also referred to as LOR) for the annihilation event.
However, in a practical PET system, before a pair of γ photons in opposite directions produced by the annihilation reach the detector, there would be a certain probability of occurrence of the Compton Effect in a biological tissue, resulting in the loss of energy and the change in the traveling direction of the γ photons. Finally the position where the γ photons are detected on the detector shifts. In this case, the detector may obtain an incorrect LOR for the annihilation event when using the actual incident position of the pair of γ photons. This is referred to as Scattering Events. To discriminate the scattering events, the usual practice is to employ energy coincidence, that is, setting up an energy window with the lowest and highest thresholds to determine each of the scintillation pulse events, and filtering out the event beyond the thresholds of the energy window.
In an ideal case, for the scintillation pulse event of each crystal strip of the array of the detector, energy statistics is made to obtain the same energy spectrum. However, in a practical implementation, since the gains of the photoelectric conversion devices are different and the resistor-weighted network used in the subsequent stage to determine the position has inconsistent energy responses regarding different positions, it is impossible to directly employ the same energy window to perform filtering. Instead, the data on crystal segmentation need to be taken into account, statistics for its energy spectrum information should be made, the energy of the scintillation pulse for each of the crystal strips is corrected to 511 keV, and then the energy window is used to perform energy coincidence.
There are several ways to achieve these processes, which are listed below.
The ClearPET detection modules developed by References document [1] produce single event frames. The single event frame must firstly be transmitted to multiple pretreatment personal computer (hereinafter simply referred to as PC) where crystal searching and energy correction operations are performed. The data suffering the energy coincidence then is transmitted to the main PC for the subsequent data process. However this method can not improve the effective data bandwidth of a single detection module. The highest counting rate that can be achieved by the single detection module is 478 kevents/s.
The miniPET developed by References document [2] and [3] uses a soft coincidence method. That is, after being formed by the detection module, the single event frame is transmitted to a host computer via Ethernet for coincidence detection. The crystal segmentation and energy correction operations are achieved via hardware according to this method. Accuracy, adaptability and scalability are poor. The counting rate that can be achieved by the single detection module is 90 kevents/s/detector.
The above methods can not meet the demand for the high counting rate required by the all-digital PET. Accordingly, for the above technical problems, there is a need for a full automatic method of online energy coincidence based on all-digital PET system to overcome the above disadvantages.    [1] Streun, M.; Brandenburg, G; Lame, H.; Parl, C.; Ziemons, K., “The data acquisition system of ClearPET neuro—a small animal PET scanner,” Nuclear Science, IEEE Transactions on, vol. 53, no. 3, pp. 700, 703, June 2006.    [2] Hegyesi, G; Imrek, J.; Kalinka, G; Molnar, J.; Novak, D.; Végh, J.; Balkay, L.; Emri, M.; Kis, A.; Molnar, G; Tron, L.; Valastyan, I.; Bagamery, I.; Bukki, T.; Rozsa, S.; Szabo, Z.; Kerek, A., “Ethernet Based Distributed Data Acquisition System for a Small Animal PET,” Nuclear Science, IEEE Transactions on, vol. 53, no. 4, pp. 2112, 2117, August 2006.    [3] Hegyesi, G; Imrek, J.; Kalinka, G; Molnar, J.; Novak, D.; Végh, J.; Balkay, L.; Emri, M.; Molnar, G; Tron, L.; Bagamery, I.; Bukki, T.; Rozsa, S.; Szabo, Z.; Kerek, A., “Development of an FPGA-based data acquisition module for small animal PET,” Nuclear Science Symposium Conference Record, 2004 IEEE, vol. 5, no., pp. 2957,2961, 16-22 Oct. 2004.